IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i14p10783-d1190357.html
   My bibliography  Save this article

A Novel Robotic-Vision-Based Defect Inspection System for Bracket Weldments in a Cloud–Edge Coordination Environment

Author

Listed:
  • Hao Li

    (Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

  • Xiaocong Wang

    (Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

  • Yan Liu

    (School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

  • Gen Liu

    (Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

  • Zhongshang Zhai

    (Tianjin Miracle Intelligent Equipment Co., Ltd., Tianjin 300131, China)

  • Xinyu Yan

    (Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

  • Haoqi Wang

    (Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

  • Yuyan Zhang

    (Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

Abstract

Arc-welding robots are widely used in the production of automotive bracket parts. The large amounts of fumes and toxic gases generated during arc welding can affect the inspection results, as well as causing health problems, and the product needs to be sent to an additional checkpoint for manual inspection. In this work, the framework of a robotic-vision-based defect inspection system was proposed and developed in a cloud–edge computing environment, which can drastically reduce the manual labor required for visual inspection, minimizing the risks associated with human error and accidents. Firstly, a passive vision sensor was installed on the end joint of the arc-welding robot, the imaging module was designed to capture bracket weldments images after the arc-welding process, and datasets with qualified images were created in the production line for deep-learning-based research on steel surface defects. To enhance the detection precision, a redesigned lightweight inspection network was then employed, while a fast computation speed was ensured through the utilization of a cloud–edge-computing computational framework. Finally, virtual simulation and Internet of Things technologies were adopted to develop the inspection and control software in order to monitor the whole process remotely. The experimental results demonstrate that the proposed approach can realize the faster identification of quality issues, achieving higher steel production efficiency and economic profits.

Suggested Citation

  • Hao Li & Xiaocong Wang & Yan Liu & Gen Liu & Zhongshang Zhai & Xinyu Yan & Haoqi Wang & Yuyan Zhang, 2023. "A Novel Robotic-Vision-Based Defect Inspection System for Bracket Weldments in a Cloud–Edge Coordination Environment," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:10783-:d:1190357
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/14/10783/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/14/10783/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Varun Tripathi & Somnath Chattopadhyaya & Alok Kumar Mukhopadhyay & Shubham Sharma & Changhe Li & Gianpaolo Di Bona, 2022. "A Sustainable Methodology Using Lean and Smart Manufacturing for the Cleaner Production of Shop Floor Management in Industry 4.0," Mathematics, MDPI, vol. 10(3), pages 1-23, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhongfei Zhang & Ting Qu & Kuo Zhao & Kai Zhang & Yongheng Zhang & Lei Liu & Jun Wang & George Q. Huang, 2023. "Optimization Model and Strategy for Dynamic Material Distribution Scheduling Based on Digital Twin: A Step towards Sustainable Manufacturing," Sustainability, MDPI, vol. 15(23), pages 1-29, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang Qianqiu & Song Baoli, 2024. "Research on the impact and mechanisms of lean management on the green development of traditional small and micro manufacturing enterprises," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(5), pages 4956-4979, September.
    2. Aswathy Sreenivasan & M. Suresh, 2024. "Enabling technologies influencing the start-up operations 5.0," Operations Management Research, Springer, vol. 17(3), pages 869-890, September.
    3. Âli Yurdun Orbak & Metin Küçük & Mehmet Akansel & Shubham Sharma & Changhe Li & Raman Kumar & Sunpreet Singh & Gianpaolo Di Bona, 2023. "Mathematical Model Assisted Six-Sigma Approach for Reducing the Logistics Costs of a Pipe Manufacturing Company: A Novel Experimental Approach," Mathematics, MDPI, vol. 11(3), pages 1-18, January.
    4. Lin, Weiwen & Qin, Shan & Zhou, Xinzhu & Guan, Xin & Zeng, Yanzhao & Wang, Zeyu & Shen, Yaohan, 2024. "Three-dimensional quantitative mineral prediction from convolutional neural network model in developing intelligent cleaning technology," Resources Policy, Elsevier, vol. 88(C).
    5. Xiaoyu Wen & Qingbo Song & Yunjie Qian & Dongping Qiao & Haoqi Wang & Yuyan Zhang & Hao Li, 2023. "Effective Improved NSGA-II Algorithm for Multi-Objective Integrated Process Planning and Scheduling," Mathematics, MDPI, vol. 11(16), pages 1-17, August.
    6. Valentina De Simone & Valentina Di Pasquale & Maria Elena Nenni & Salvatore Miranda, 2023. "Sustainable Production Planning and Control in Manufacturing Contexts: A Bibliometric Review," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    7. Varun Tripathi & Somnath Chattopadhyaya & Alok Kumar Mukhopadhyay & Shubham Sharma & Changhe Li & Sunpreet Singh & Waqas Ul Hussan & Bashir Salah & Waqas Saleem & Abdullah Mohamed, 2022. "A Sustainable Productive Method for Enhancing Operational Excellence in Shop Floor Management for Industry 4.0 Using Hybrid Integration of Lean and Smart Manufacturing: An Ingenious Case Study," Sustainability, MDPI, vol. 14(12), pages 1-21, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:10783-:d:1190357. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.